diff --git a/examples/data/mem_cube_tree/textual_memory.json b/examples/data/mem_cube_tree/textual_memory.json new file mode 100644 index 000000000..91f426ca2 --- /dev/null +++ b/examples/data/mem_cube_tree/textual_memory.json @@ -0,0 +1,4219 @@ +{ + "nodes": [ + { + "id": "c1e2bfb3-4dd7-40f9-a347-a25a72223b2c", + "memory": "On January 13, 2026, at 8:51 PM, the user added another memory to demonstrate the concept of persistence.", + "metadata": { + "sources": [ + "\"{\\\"type\\\": \\\"chat\\\", \\\"role\\\": \\\"user\\\", \\\"chat_time\\\": \\\"08:51 PM on 13 January, 2026\\\", \\\"content\\\": \\\"This is a test memory for dump example\\\", \\\"lang\\\": \\\"en\\\"}\"", + "\"{\\\"type\\\": \\\"chat\\\", \\\"role\\\": \\\"user\\\", \\\"chat_time\\\": \\\"08:51 PM on 13 January, 2026\\\", \\\"content\\\": \\\"Another memory to demonstrate persistence\\\", \\\"lang\\\": \\\"en\\\"}\"" + ], + "confidence": 0.99, + "usage": [], + "created_at": "2026-01-13T20:51:42.394870000+00:00", + "session_id": "default_session", + "memory_type": "UserMemory", + "type": "fact", + "tags": [ + "persistence", + "memory", + "demonstration" + ], + "updated_at": "2026-01-13T20:51:42.396309000+00:00", + "user_id": "example_user", + "background": "On January 13, 2026, at 8:51 PM, the user engaged in a memory demonstration by creating a test memory example and another memory to illustrate the concept of persistence. 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+ "persistence", + "memory", + "demonstration" + ], + "updated_at": "2026-01-13T20:51:42.396281000+00:00", + "user_id": "example_user", + "background": "On January 13, 2026, at 8:51 PM, the user engaged in a memory demonstration by creating a test memory example and another memory to illustrate the concept of persistence. These actions reflect the user's interest in exploring and demonstrating memory-related concepts.", + "file_ids": [], + "embedding": [ + -0.022425159811973572, + 0.01004026923328638, + 0.013415387831628323, + -0.013093948364257812, + -0.04768655076622963, + -0.047837819904088974, + 0.02147974818944931, + 0.019097311422228813, + 0.005332120228558779, + -0.005180854815989733, + 0.0216499213129282, + -0.006977136246860027, + -0.014067722484469414, + 0.014010997489094734, + -0.01164746843278408, + 0.037948813289403915, + 0.049539558589458466, + -0.028778323903679848, + -0.0363227054476738, + -0.006282258778810501, + 0.03201163187623024, + -0.043678008019924164, + 0.06640569865703583, + 0.04734620451927185, + -0.03552856296300888, + 0.008948319591581821, + -0.018199170008301735, + 0.015788370743393898, + 0.08047342300415039, + 0.04632515832781792, + -0.01887986622750759, + 0.00966683216392994, + -0.02516685239970684, + -0.00974719226360321, + -0.01631780155003071, + -0.01085332315415144, + 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These actions reflect the user's interest in exploring and demonstrating memory-related concepts.", + "file_ids": [], + "embedding": [ + -0.023225771263241768, + 0.009711744263768196, + 0.01401975005865097, + -0.023113388568162918, + -0.044953107833862305, + -0.025454696267843246, + 0.004780950024724007, + 0.012184164486825466, + 0.003397237276658416, + 0.006766378879547119, + 0.033059265464544296, + -0.0018625102238729596, + -0.00135678774677217, + -0.0159396231174469, + -0.0017864176770672202, + 0.03302180394530296, + 0.05941302329301834, + -0.013467201963067055, + -0.011341294273734093, + -0.03324656933546066, + 0.01599581353366375, + -0.05263259634375572, + 0.07222465425729752, + 0.022851161658763885, + -0.007220592815428972, + 0.0017969536129385233, + -0.012418295256793499, + 0.013214340433478355, + 0.08645980805158615, + 0.0018215373856946826, + 0.02071589045226574, + -0.028620144352316856, + -0.015490091405808926, + -0.025454696267843246, + -0.04469088092446327, + 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These actions reflect the user's interest in exploring and demonstrating memory-related concepts.", + "file_ids": [], + "embedding": [ + -0.023225771263241768, + 0.009711744263768196, + 0.01401975005865097, + -0.023113388568162918, + -0.044953107833862305, + -0.025454696267843246, + 0.004780950024724007, + 0.012184164486825466, + 0.003397237276658416, + 0.006766378879547119, + 0.033059265464544296, + -0.0018625102238729596, + -0.00135678774677217, + -0.0159396231174469, + -0.0017864176770672202, + 0.03302180394530296, + 0.05941302329301834, + -0.013467201963067055, + -0.011341294273734093, + -0.03324656933546066, + 0.01599581353366375, + -0.05263259634375572, + 0.07222465425729752, + 0.022851161658763885, + -0.007220592815428972, + 0.0017969536129385233, + -0.012418295256793499, + 0.013214340433478355, + 0.08645980805158615, + 0.0018215373856946826, + 0.02071589045226574, + -0.028620144352316856, + -0.015490091405808926, + -0.025454696267843246, + -0.04469088092446327, + 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0.0029125867877155542, + -0.019123800098896027, + -0.04903634637594223, + -0.02921951934695244 + ], + "key": "Test Memory Example", + "status": "activated" + } + } + ], + "edges": [], + "total_nodes": 4, + "total_edges": 0 +} \ No newline at end of file diff --git a/examples/mem_cube/_deprecated/README.md b/examples/mem_cube/_deprecated/README.md new file mode 100644 index 000000000..da02eebef --- /dev/null +++ b/examples/mem_cube/_deprecated/README.md @@ -0,0 +1,81 @@ +# Deprecated Examples + +⚠️ **These examples are deprecated and no longer maintained.** + +## Why deprecated? + +These examples demonstrate old APIs that directly access MemCube internals (e.g., `mem_cube.text_mem.get_all()`), which is no longer the recommended approach. + +## Current Best Practice + +**Use `SingleCubeView` / `CompositeCubeView` for all add/search operations.** + +The new View architecture provides: +- ✅ Unified API interface +- ✅ Multi-cube support +- ✅ Better integration with MemOS Server +- ✅ Consistent result format with `cube_id` tracking + +## Updated Examples + +See the following files in the parent directory: +- **`../load_cube.py`** - Load MemCube and operate via SingleCubeView +- **`../dump_cube.py`** - Persist MemCube to disk + +## Migration Guide + +### Old approach (deprecated): +```python +mem_cube = GeneralMemCube.init_from_dir("examples/data/mem_cube_2") +items = mem_cube.text_mem.get_all() # ❌ Direct access +for item in items: + print(item) +``` + +### New approach (recommended): +```python +import json +from memos.api.handlers import init_server +from memos.api.product_models import APISearchRequest +from memos.multi_mem_cube.single_cube import SingleCubeView +from memos.log import get_logger + +logger = get_logger(__name__) + +# Initialize server (uses .env configuration) +components = init_server() +naive = components["naive_mem_cube"] + +# Create View +view = SingleCubeView( + cube_id="my_cube", + naive_mem_cube=naive, + mem_reader=components["mem_reader"], + mem_scheduler=components["mem_scheduler"], + logger=logger, + searcher=components["searcher"], + feedback_server=components["feedback_server"], +) + +# Load data from exported JSON +with open("examples/data/mem_cube_tree/textual_memory.json") as f: + json_data = json.load(f) +naive.text_mem.graph_store.import_graph(json_data, user_name="my_cube") + +# Use View API for search +results = view.search_memories(APISearchRequest( + user_id="user", + readable_cube_ids=["my_cube"], + query="your query here", +)) +for group in results.get("text_mem", []): + for mem in group.get("memories", []): + print(mem.get("metadata", {}).get("memory", "N/A")) +``` + +> **Note on Embeddings**: The sample data uses **bge-m3** model with **1024 dimensions**. +> Ensure your environment uses the same embedding configuration for accurate search. + +--- + +For more information, see the [MemCube documentation](https://memos-doc.memoryos.ai/open_source/modules/mem_cube). diff --git a/examples/mem_cube/load_from_folder.py b/examples/mem_cube/_deprecated/load_from_folder.py similarity index 100% rename from examples/mem_cube/load_from_folder.py rename to examples/mem_cube/_deprecated/load_from_folder.py diff --git a/examples/mem_cube/load_from_remote.py b/examples/mem_cube/_deprecated/load_from_remote.py similarity index 100% rename from examples/mem_cube/load_from_remote.py rename to examples/mem_cube/_deprecated/load_from_remote.py diff --git a/examples/mem_cube/load_lazily.py b/examples/mem_cube/_deprecated/load_lazily.py similarity index 100% rename from examples/mem_cube/load_lazily.py rename to examples/mem_cube/_deprecated/load_lazily.py diff --git a/examples/mem_cube/dump_cube.py b/examples/mem_cube/dump_cube.py new file mode 100644 index 000000000..5a2967ff9 --- /dev/null +++ b/examples/mem_cube/dump_cube.py @@ -0,0 +1,137 @@ +""" +MemCube dump example using SingleCubeView. + +Demonstrates: +1. Initialize server and create SingleCubeView with NEW cube_id +2. Add memories via View +3. Dump ONLY this cube's data to directory + +Requirements: + - MemOS service environment (.env configured) + - Neo4j graph database (set NEO4J_BACKEND=neo4j in .env) + +Note on Embeddings: + This example exports embeddings along with memory data. + The sample data uses: bge-m3 model, 1024 dimensions. + If your environment uses a different embedding model or dimension, + you may need to re-embed the data after import, or the semantic + search results may be inaccurate or fail. +""" + +import contextlib +import json +import os +import shutil + +from memos.api.handlers import init_server +from memos.api.product_models import APIADDRequest +from memos.log import get_logger +from memos.multi_mem_cube.single_cube import SingleCubeView + + +logger = get_logger(__name__) + +# NEW cube_id to avoid dumping existing data +EXAMPLE_CUBE_ID = "example_dump_cube" +EXAMPLE_USER_ID = "example_user" + +# ============================================================================= +# Step 1: Initialize server +# ============================================================================= +print("=" * 60) +print("Step 1: Initialize server") +print("=" * 60) + +components = init_server() +print("✓ Server initialized") + +# ============================================================================= +# Step 2: Create SingleCubeView with NEW cube_id +# ============================================================================= +print("\n" + "=" * 60) +print(f"Step 2: Create SingleCubeView (cube_id={EXAMPLE_CUBE_ID})") +print("=" * 60) + +naive = components["naive_mem_cube"] +view = SingleCubeView( + cube_id=EXAMPLE_CUBE_ID, # NEW cube_id + naive_mem_cube=naive, + mem_reader=components["mem_reader"], + mem_scheduler=components["mem_scheduler"], + logger=logger, + searcher=components["searcher"], + feedback_server=components["feedback_server"], +) +print("✓ SingleCubeView created") + +# ============================================================================= +# Step 3: Add memories via View +# ============================================================================= +print("\n" + "=" * 60) +print("Step 3: Add memories via SingleCubeView") +print("=" * 60) + +result = view.add_memories( + APIADDRequest( + user_id=EXAMPLE_USER_ID, + writable_cube_ids=[EXAMPLE_CUBE_ID], + messages=[ + {"role": "user", "content": "This is a test memory for dump example"}, + {"role": "user", "content": "Another memory to demonstrate persistence"}, + ], + async_mode="sync", + ) +) +print(f"✓ Added {len(result)} memories") + +# ============================================================================= +# Step 4: Dump ONLY this cube's data +# ============================================================================= +print("\n" + "=" * 60) +print("Step 4: Dump cube data (filtered by cube_id)") +print("=" * 60) + +output_dir = "tmp/mem_cube_dump" +if os.path.exists(output_dir): + shutil.rmtree(output_dir) +os.makedirs(output_dir, exist_ok=True) + +# Export only this cube's data using user_name filter +text_mem = naive.text_mem +json_data = text_mem.graph_store.export_graph( + include_embedding=True, # Include embeddings for semantic search + user_name=EXAMPLE_CUBE_ID, # Filter by cube_id +) + +# Fix embedding format: parse string to list for import compatibility +# (export_graph stores embedding as string in metadata, but add_node expects list) +for node in json_data.get("nodes", []): + metadata = node.get("metadata", {}) + if "embedding" in metadata and isinstance(metadata["embedding"], str): + with contextlib.suppress(json.JSONDecodeError): + metadata["embedding"] = json.loads(metadata["embedding"]) + +print(f"✓ Exported {len(json_data.get('nodes', []))} nodes") + +# Save to file +memory_file = os.path.join(output_dir, "textual_memory.json") +with open(memory_file, "w", encoding="utf-8") as f: + json.dump(json_data, f, indent=2, ensure_ascii=False) +print(f"✓ Saved to: {memory_file}") + +# Save config (user can modify sensitive fields before sharing) +config = components["default_cube_config"].model_copy(deep=True) +config.user_id = EXAMPLE_USER_ID +config.cube_id = EXAMPLE_CUBE_ID +config_file = os.path.join(output_dir, "config.json") +config.to_json_file(config_file) +print(f"✓ Config saved to: {config_file}") + +# ============================================================================= +# Done +# ============================================================================= +print("\n" + "=" * 60) +print("✅ Example completed!") +print("=" * 60) +print(f"\nDumped to: {output_dir}") +print("Run load_cube.py to load this data") diff --git a/examples/mem_cube/load_cube.py b/examples/mem_cube/load_cube.py new file mode 100644 index 000000000..c7552a4a2 --- /dev/null +++ b/examples/mem_cube/load_cube.py @@ -0,0 +1,137 @@ +""" +MemCube load example using SingleCubeView. + +Demonstrates: +1. Initialize server and create SingleCubeView +2. Load memories from dump via graph_store.import_graph() +3. Display loaded memories +4. Search loaded memories (semantic search) + +Requirements: + - MemOS service environment (.env configured) + - Neo4j graph database (set NEO4J_BACKEND=neo4j in .env) + +Note on Embeddings: + The sample data (examples/data/mem_cube_tree) uses: bge-m3 model, 1024 dimensions. + For semantic search to work correctly, your environment must use the same + embedding model and dimension. If different, search results may be inaccurate. +""" + +import json +import os + +from memos.api.handlers import init_server +from memos.api.product_models import APISearchRequest +from memos.log import get_logger +from memos.multi_mem_cube.single_cube import SingleCubeView + + +logger = get_logger(__name__) + +EXAMPLE_CUBE_ID = "example_dump_cube" +EXAMPLE_USER_ID = "example_user" + +# ============================================================================= +# Step 1: Initialize server +# ============================================================================= +print("=" * 60) +print("Step 1: Initialize server") +print("=" * 60) + +components = init_server() +print("✓ Server initialized") + +# ============================================================================= +# Step 2: Create SingleCubeView +# ============================================================================= +print("\n" + "=" * 60) +print(f"Step 2: Create SingleCubeView (cube_id={EXAMPLE_CUBE_ID})") +print("=" * 60) + +naive = components["naive_mem_cube"] +view = SingleCubeView( + cube_id=EXAMPLE_CUBE_ID, + naive_mem_cube=naive, + mem_reader=components["mem_reader"], + mem_scheduler=components["mem_scheduler"], + logger=logger, + searcher=components["searcher"], + feedback_server=components["feedback_server"], +) +print("✓ SingleCubeView created") + +# ============================================================================= +# Step 3: Load memories from dump +# ============================================================================= +print("\n" + "=" * 60) +print("Step 3: Load memories from dump") +print("=" * 60) + +load_dir = "examples/data/mem_cube_tree" +memory_file = os.path.join(load_dir, "textual_memory.json") + +if not os.path.exists(memory_file): + print(f"❌ File not found: {memory_file}") + print(" Run dump_cube.py first to create data!") + exit(1) + +with open(memory_file, encoding="utf-8") as f: + json_data = json.load(f) + +# Import graph data into graph_store +text_mem = naive.text_mem +text_mem.graph_store.import_graph(json_data, user_name=EXAMPLE_CUBE_ID) + +nodes = json_data.get("nodes", []) +edges = json_data.get("edges", []) +print(f"✓ Imported {len(nodes)} nodes, {len(edges)} edges") + +# ============================================================================= +# Step 4: Display loaded memories +# ============================================================================= +print("\n" + "=" * 60) +print("Step 4: Display loaded memories") +print("=" * 60) + +print(f"\nLoaded {len(nodes)} memories:") +for i, node in enumerate(nodes, 1): + metadata = node.get("metadata", {}) + memory_text = node.get("memory", "N/A") + mem_type = metadata.get("memory_type", "unknown") + print(f"\n [{i}] Type: {mem_type}") + print(f" Content: {memory_text[:70]}...") + +# ============================================================================= +# Step 5: Search loaded memories +# ============================================================================= +print("\n" + "=" * 60) +print("Step 5: Search loaded memories") +print("=" * 60) + +query = "test memory dump persistence demonstration" +print(f'Query: "{query}"') + +search_result = view.search_memories( + APISearchRequest( + user_id=EXAMPLE_USER_ID, + readable_cube_ids=[EXAMPLE_CUBE_ID], + query=query, + ) +) + +text_mem_results = search_result.get("text_mem", []) +memories = [] +for group in text_mem_results: + memories.extend(group.get("memories", [])) + +print(f"\n✓ Found {len(memories)} relevant memories:") +for i, mem in enumerate(memories[:3], 1): + content = mem.get("metadata", {}).get("memory", "N/A")[:70] + print(f" [{i}] {content}...") + +# ============================================================================= +# Done +# ============================================================================= +print("\n" + "=" * 60) +print("✅ Example completed!") +print("=" * 60)